
import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowercase    :   Any						=			logging.get_logger(__name__)

lowercase    :   Union[str, Any]						=			{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowercase    :   Dict						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     Any		,    _lowerCamelCase					:     int		,    _lowerCamelCase					:     List[str]		,    _lowerCamelCase					:     str)   ->       int:






							'''simple docstring'''





							for attribute in key.split("."):
														__UpperCamelCase      :  Dict       =   getattr(_lowerCamelCase		,    _lowerCamelCase)

							if weight_type is not None:
														__UpperCamelCase      :  Any       =   getattr(_lowerCamelCase		,    _lowerCamelCase).shape
							else:
														__UpperCamelCase      :  List[Any]       =   hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
														    F' {value.shape} for {full_name}')

							if weight_type == "weight":
														__UpperCamelCase      :  Any       =   value
							elif weight_type == "weight_g":
														__UpperCamelCase      :  int       =   value
							elif weight_type == "weight_v":
														__UpperCamelCase      :  str       =   value
							elif weight_type == "bias":
														__UpperCamelCase      :  List[str]       =   value
							else:
														__UpperCamelCase      :  Optional[int]       =   value

							logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.')




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Tuple		,    _lowerCamelCase					:     Tuple)   ->       str:






							'''simple docstring'''





							__UpperCamelCase      :  int       =   []
							__UpperCamelCase      :  Dict       =   fairseq_model.state_dict()

							__UpperCamelCase      :  Tuple       =   hf_model.unispeech_sat.feature_extractor

							for name, value in fairseq_dict.items():
														__UpperCamelCase      :  str       =   False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    hf_model.config.feat_extract_norm == "group"		,    )
																					__UpperCamelCase      :  Optional[Any]       =   True
														else:
																					for key, mapped_key in MAPPING.items():
																												__UpperCamelCase      :  Union[str, Any]       =   "unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																																			if "layer_norm_for_extract" in name and (".".join(name.split(".")[:-1]) != key):
																																										# special case since naming is very similar
																																										continue
																																			__UpperCamelCase      :  List[str]       =   True
																																			if "*" in mapped_key:
																																										__UpperCamelCase      :  Dict       =   name.split(_lowerCamelCase)[0].split(".")[-2]
																																										__UpperCamelCase      :  Optional[Any]       =   mapped_key.replace("*"		,    _lowerCamelCase)
																																			if "weight_g" in name:
																																										__UpperCamelCase      :  List[Any]       =   "weight_g"
																																			elif "weight_v" in name:
																																										__UpperCamelCase      :  str       =   "weight_v"
																																			elif "bias" in name:
																																										__UpperCamelCase      :  Union[str, Any]       =   "bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										__UpperCamelCase      :  Dict       =   "weight"
																																			else:
																																										__UpperCamelCase      :  Any       =   None
																																			set_recursively(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase)
																												continue
														if not is_used:
																					unused_weights.append(_lowerCamelCase)

							logger.warning(F'Unused weights: {unused_weights}')




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int		,    _lowerCamelCase					:     Optional[Any]		,    _lowerCamelCase					:     Union[str, Any]		,    _lowerCamelCase					:     Any		,    _lowerCamelCase					:     Dict)   ->       Optional[int]:






							'''simple docstring'''





							__UpperCamelCase      :  Dict       =   full_name.split("conv_layers.")[-1]
							__UpperCamelCase      :  List[str]       =   name.split(".")
							__UpperCamelCase      :  int       =   int(items[0])
							__UpperCamelCase      :  Tuple       =   int(items[1])

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.')
																					__UpperCamelCase      :  Optional[Any]       =   value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.')
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.')
																					__UpperCamelCase      :  Any       =   value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.')
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.')
																					__UpperCamelCase      :  str       =   value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.')
																					__UpperCamelCase      :  Optional[Any]       =   value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
							else:
														unused_weights.append(_lowerCamelCase)




@torch.no_grad()
def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     Any		,    _lowerCamelCase					:     List[Any]		,    _lowerCamelCase					:     Optional[int]=None		,    _lowerCamelCase					:     int=None		,    _lowerCamelCase					:     Optional[Any]=True)   ->       Any:






							'''simple docstring'''





							if config_path is not None:
														__UpperCamelCase      :  str       =   UniSpeechSatConfig.from_pretrained(_lowerCamelCase)
							else:
														__UpperCamelCase      :  int       =   UniSpeechSatConfig()

							__UpperCamelCase      :  str       =   ""

							if is_finetuned:
														__UpperCamelCase      :  str       =   UniSpeechSatForCTC(_lowerCamelCase)
							else:
														__UpperCamelCase      :  int       =   UniSpeechSatForPreTraining(_lowerCamelCase)

							__UpperCamelCase  ,		__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]		,    arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
							__UpperCamelCase      :  str       =   model[0].eval()

							recursively_load_weights(_lowerCamelCase		,    _lowerCamelCase)

							hf_wavavec.save_pretrained(_lowerCamelCase)


if __name__ == "__main__":
		lowercase    :   Dict						=			argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		lowercase    :   str						=			parser.parse_args()
		convert_unispeech_sat_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)

from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase    :   Union[str, Any]						=			logging.get_logger(__name__)

lowercase    :   Union[str, Any]						=			{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	_A								=	'efficientformer'







	def __init__(   self						:Union[str, Any]	,  a						:List[int] = [3, 2, 6, 4]	,  a						:List[int] = [4_8, 9_6, 2_2_4, 4_4_8]	,  a						:List[bool] = [True, True, True, True]	,  a						:int = 4_4_8	,  a						:int = 3_2	,  a						:int = 4	,  a						:int = 7	,  a						:int = 5	,  a						:int = 8	,  a						:int = 4	,  a						:float = 0.0	,  a						:int = 1_6	,  a						:int = 3	,  a						:int = 3	,  a						:int = 3	,  a						:int = 2	,  a						:int = 1	,  a						:float = 0.0	,  a						:int = 1	,  a						:bool = True	,  a						:bool = True	,  a						:float = 1E-5	,  a						:str = "gelu"	,  a						:float = 0.02	,  a						:float = 1E-1_2	,  a						:int = 2_2_4	,  a						:float = 1E-0_5	,  **a						:str	,  )						->					None:
								super().__init__(**a		)

								__UpperCamelCase      :  Optional[Any]       =   hidden_act
								__UpperCamelCase      :  Any       =   hidden_dropout_prob
								__UpperCamelCase      :  str       =   hidden_sizes
								__UpperCamelCase      :  Dict       =   num_hidden_layers
								__UpperCamelCase      :  Optional[Any]       =   num_attention_heads
								__UpperCamelCase      :  str       =   initializer_range
								__UpperCamelCase      :  List[str]       =   layer_norm_eps
								__UpperCamelCase      :  str       =   patch_size
								__UpperCamelCase      :  str       =   num_channels
								__UpperCamelCase      :  Any       =   depths
								__UpperCamelCase      :  Tuple       =   mlp_expansion_ratio
								__UpperCamelCase      :  List[str]       =   downsamples
								__UpperCamelCase      :  Optional[int]       =   dim
								__UpperCamelCase      :  Dict       =   key_dim
								__UpperCamelCase      :  Optional[Any]       =   attention_ratio
								__UpperCamelCase      :  Dict       =   resolution
								__UpperCamelCase      :  Union[str, Any]       =   pool_size
								__UpperCamelCase      :  Tuple       =   downsample_patch_size
								__UpperCamelCase      :  Optional[int]       =   downsample_stride
								__UpperCamelCase      :  Optional[int]       =   downsample_pad
								__UpperCamelCase      :  Union[str, Any]       =   drop_path_rate
								__UpperCamelCase      :  Union[str, Any]       =   num_metaad_blocks
								__UpperCamelCase      :  List[str]       =   distillation
								__UpperCamelCase      :  str       =   use_layer_scale
								__UpperCamelCase      :  Tuple       =   layer_scale_init_value
								__UpperCamelCase      :  str       =   image_size
								__UpperCamelCase      :  int       =   batch_norm_eps